Summary
Full Transcript
Enroll for free: https://bit.ly/3ZMFGYE We're excited to introduce Retrieval Optimization: From Tokenization to Vector Quantization, a short course made in collaboration with Qdrant, and taught by Kacper Łukawski, its Developer Relations Lead. In this course, you'll learn about tokenization and vector search optimization for large-scale customer-facing RAG applications. You'll learn about the technical details of how vector search works and how to optimize it for better performance. By the end of this course, you'll have a solid understanding of how tokenization is done and how to optimize vector search in your RAG systems. Here's what you'll learn, in detail: - Understand the internal workings of the embedding model and how your text is turned into vectors. - Explore different tokenization techniques like Byte-Pair Encoding, WordPiece, and Unigram, and how they affect search relevancy. - Learn how to measure the quality of your search across several quality metrics. - Understand how the main parameters in HNSW algorithms affect the relevance and speed of vector search and how to optimally adjust these parameters. - Experiment with the three major quantization methods, product, scalar, and binary, and learn how they impact memory requirements, search quality, and speed. Join in and take your RAG applications to the next level! Learn more: https://bit.ly/3ZMFGYE
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
